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--- |
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license: mit |
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base_model: google/vivit-b-16x2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: vivit-b-16x2-finetuned-cctv-surveillance |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vivit-b-16x2-finetuned-cctv-surveillance |
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This model is a fine-tuned version of [google/vivit-b-16x2](https://huggingface.co/google/vivit-b-16x2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1478 |
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- Accuracy: 0.9460 |
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- F1: 0.9430 |
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- Recall: 0.9460 |
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- Precision: 0.9454 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 4176 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.9564 | 0.12 | 522 | 0.4417 | 0.8685 | 0.8096 | 0.8685 | 0.7990 | |
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| 0.4574 | 1.12 | 1044 | 0.2633 | 0.9131 | 0.9042 | 0.9131 | 0.9269 | |
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| 0.421 | 2.12 | 1566 | 0.1875 | 0.9272 | 0.9100 | 0.9272 | 0.9353 | |
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| 0.4785 | 3.12 | 2088 | 0.1854 | 0.9249 | 0.9082 | 0.9249 | 0.9140 | |
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| 0.3213 | 4.12 | 2610 | 0.1805 | 0.9272 | 0.9125 | 0.9272 | 0.9216 | |
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| 0.1465 | 5.12 | 3132 | 0.1733 | 0.9413 | 0.9362 | 0.9413 | 0.9398 | |
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| 0.0784 | 6.12 | 3654 | 0.1616 | 0.9437 | 0.9391 | 0.9437 | 0.9434 | |
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| 0.2017 | 7.12 | 4176 | 0.1478 | 0.9460 | 0.9430 | 0.9460 | 0.9454 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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